Top Modules Exploratory Analysis
#setwd('/afs/inf.ed.ac.uk/user/s17/s1725186/Documents/PhD-Models/FirstPUModel/RMarkdowns')
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly) ; library(dendextend)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally)
library(expss)
library(polycor)
library(foreach) ; library(doParallel)
library(knitr)
library(biomaRt)
library(anRichment) ; library(BrainDiseaseCollection)
suppressWarnings(suppressMessages(library(WGCNA)))
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}Load preprocessed dataset (preprocessing code in 20_02_21_data_preprocessing.Rmd) and clustering (pipeline in 20_02_24_WGCNA.Rmd)
# Gandal dataset
load('./../Data/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
DE_info = DE_info %>% data.frame
# GO Neuronal annotations: regex 'neuron' in GO functional annotations and label the genes that make a match as neuronal
GO_annotations = read.csv('./../Data/genes_GO_annotations.csv')
GO_neuronal = GO_annotations %>% filter(grepl('neuron', go_term)) %>%
mutate('ID'=as.character(ensembl_gene_id)) %>%
dplyr::select(-ensembl_gene_id) %>% distinct(ID) %>%
mutate('Neuronal'=1)
# SFARI Genes
SFARI_genes = read_csv('./../../../SFARI/Data/SFARI_genes_08-29-2019_w_ensembl_IDs.csv')
SFARI_genes = SFARI_genes[!duplicated(SFARI_genes$ID) & !is.na(SFARI_genes$ID),]
# Clusterings
clusterings = read_csv('./../Data/clusters.csv')
# Update DE_info with SFARI and Neuronal information
genes_info = DE_info %>% mutate('ID'=rownames(.)) %>% left_join(SFARI_genes, by='ID') %>%
mutate(`gene-score`=ifelse(is.na(`gene-score`), 'None', `gene-score`)) %>%
left_join(GO_neuronal, by='ID') %>% left_join(clusterings, by='ID') %>%
mutate(Neuronal=ifelse(is.na(Neuronal), 0, Neuronal)) %>%
mutate(gene.score=ifelse(`gene-score`=='None' & Neuronal==1, 'Neuronal', `gene-score`),
significant=padj<0.05 & !is.na(padj))
# Add gene symbol
getinfo = c('ensembl_gene_id','external_gene_id')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl',
host='feb2014.archive.ensembl.org') ## Gencode v19
gene_names = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=genes_info$ID, mart=mart)
genes_info = genes_info %>% left_join(gene_names, by=c('ID'='ensembl_gene_id'))
clustering_selected = 'DynamicHybridMergedSmall'
genes_info$Module = genes_info[,clustering_selected]
dataset = read.csv(paste0('./../Data/dataset_', clustering_selected, '.csv'))
dataset$Module = dataset[,clustering_selected]
rm(DE_info, GO_annotations, clusterings, getinfo, mart, dds)Relation to external clinical traits
Quantifying module-trait associations
Using the hetcor function, that calculates Pearson, polyserial or polychoric correlations depending on the type of variables involved.
datTraits = datMeta %>% dplyr::select(Diagnosis, Sex, Age, PMI, RNAExtractionBatch) %>%
dplyr::rename('ExtractionBatch' = RNAExtractionBatch)
# Recalculate MEs with color labels
ME_object = datExpr %>% t %>% moduleEigengenes(colors = genes_info$Module)
MEs = orderMEs(ME_object$eigengenes)
# Calculate correlation between eigengenes and the traits and their p-values
moduleTraitCor = MEs %>% apply(2, function(x) hetcor(x, datTraits)$correlations[1,-1]) %>% t
rownames(moduleTraitCor) = colnames(MEs)
colnames(moduleTraitCor) = colnames(datTraits)
moduleTraitPvalue = corPvalueStudent(moduleTraitCor, nrow(datExpr))
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
# In case there are any NAs
if(sum(!complete.cases(moduleTraitCor))>0){
print(paste0(sum(is.na(moduleTraitCor)),' correlation(s) could not be calculated'))
}## [1] "2 correlation(s) could not be calculated"rm(ME_object)Note: The correlation between Module #E08B00 and Diagonsis is the one that cannot be calculated, weirdly enough, the thing that causes the error is that the initial correlation is too high, so it would be a very bad thing to lose this module because of this numerical error. I’m going to fill in its value using the polyserial function, which doesn’t give exactly the same results as the hetcor() function, but it’s quite similar.
# Calculate the correlation tha failed with hetcor()
moduleTraitCor['ME#E08B00','Diagnosis'] = polyserial(MEs[,'ME#E08B00'], datTraits$Diagnosis)## Warning in polyserial(MEs[, "ME#E08B00"], datTraits$Diagnosis): initial
## correlation inadmissible, 1.07478833176392, set to 0.9999I’m going to select all the modules that have an absolute correlation higher than 0.9 with Diagnosis to study them
# Sort moduleTraitCor by Diagnosis
moduleTraitCor = moduleTraitCor[order(moduleTraitCor[,1], decreasing=TRUE),]
moduleTraitPvalue = moduleTraitPvalue[order(moduleTraitCor[,1], decreasing=TRUE),]
# Create text matrix for the Heatmap
textMatrix = paste0(signif(moduleTraitCor, 2), ' (', signif(moduleTraitPvalue, 1), ')')
dim(textMatrix) = dim(moduleTraitCor)
labeledHeatmap(Matrix = moduleTraitCor, xLabels = names(datTraits), yLabels = gsub('ME','',rownames(moduleTraitCor)),
yColorWidth=0, colors = brewer.pal(11,'PiYG'), bg.lab.y = gsub('ME','',rownames(moduleTraitCor)),
textMatrix = textMatrix, setStdMargins = FALSE, cex.text = 0.8, cex.lab.y = 0.75, zlim = c(-1,1),
main = paste('Module-Trait relationships'))
diagnosis_cor = data.frame('Module' = gsub('ME','',rownames(moduleTraitCor)),
'MTcor' = moduleTraitCor[,'Diagnosis'],
'MTpval' = moduleTraitPvalue[,'Diagnosis'])
genes_info = genes_info %>% left_join(diagnosis_cor, by='Module')
rm(moduleTraitPvalue, datTraits, textMatrix, diagnosis_cor)Studying the modules with the highest absolute correlation to Diagnosis
The modules consist mainly of points with very high (absolute) values in PC2 (which we know is related to lfc), so this result is consistent with the high correlation between Module and Diagnosis, although some of the points with the highest PC2 values do not belong to these top modules
top_modules = gsub('ME','',rownames(moduleTraitCor)[abs(moduleTraitCor[,'Diagnosis'])>0.9])
cat(paste0('Top modules selected: ', paste(top_modules, collapse=', '),'\n'))## Top modules selected: #E08B00, #7DAE00, #FE6E8Apca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, PC1, PC2, Module, gene.score) %>%
mutate(ImportantModules = ifelse(Module %in% top_modules, as.character(Module), 'Others')) %>%
mutate(color = ifelse(ImportantModules=='Others','gray',ImportantModules),
alpha = ifelse(ImportantModules=='Others', 0.2, 0.4),
gene_id = paste0(ID, ' (', external_gene_id, ')'))
table(plot_data$ImportantModules)##
## #7DAE00 #E08B00 #FE6E8A Others
## 149 223 32 15750ggplotly(plot_data %>% ggplot(aes(PC1, PC2, color=ImportantModules)) +
geom_point(alpha=plot_data$alpha, color=plot_data$color, aes(ID=gene_id)) + theme_minimal() +
ggtitle('Modules with strongest relation to Diagnosis'))rm(pca)Module Membership vs Gene Significance
create_plot = function(module){
plot_data = dataset %>% dplyr::select(ID, paste0('MM.',gsub('#','',module)), GS, gene.score) %>% filter(dataset$Module==module)
colnames(plot_data)[2] = 'Module'
SFARI_colors = as.numeric(names(table(as.character(plot_data$gene.score)[plot_data$gene.score!='None'])))
p = ggplotly(plot_data %>% ggplot(aes(Module, GS, color=gene.score)) + geom_point(alpha=0.5, aes(ID=ID)) + ylab('Gene Significance') +
scale_color_manual(values=SFARI_colour_hue(r=c(SFARI_colors,8))) + theme_minimal() + xlab('Module Membership') +
ggtitle(paste0('Module ', module,' (MTcor = ', round(moduleTraitCor[paste0('ME',module),1],2),')')))
return(p)
}
create_plot(top_modules[1])create_plot(top_modules[2])create_plot(top_modules[3])rm(create_plot)SFARI Genes
List of SFARI Genes in top modules ordered by SFARI score and Gene Significance
table_data = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, GS, gene.score, Module) %>% arrange(gene.score, desc(abs(GS))) %>%
dplyr::rename('Ensembl ID'=ID, 'Gene Symbol'=external_gene_id,
'SFARI score'=gene.score, 'Gene Significance'=GS)
kable(table_data %>% filter(Module == top_modules[1] & `SFARI score` != 'None') %>% dplyr::select(-Module),
caption=paste0('SFARI Genes for Module ', top_modules[1]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000153827 | TRIP12 | 0.6566880 | 1 |
| ENSG00000135387 | CAPRIN1 | 0.7630901 | 3 |
| ENSG00000183454 | GRIN2A | 0.6185342 | 3 |
| ENSG00000112902 | SEMA5A | 0.5854825 | 3 |
| ENSG00000197635 | DPP4 | 0.8471604 | 4 |
| ENSG00000133216 | EPHB2 | 0.8375782 | 4 |
| ENSG00000011422 | PLAUR | 0.7699256 | 4 |
| ENSG00000108055 | SMC3 | 0.7376463 | 4 |
| ENSG00000156298 | TSPAN7 | 0.4841070 | 4 |
| ENSG00000138095 | LRPPRC | 0.8541114 | 5 |
| ENSG00000254093 | PINX1 | 0.8284784 | 5 |
| ENSG00000078725 | BRINP1 | 0.7797796 | 5 |
| ENSG00000122877 | EGR2 | 0.7697789 | 5 |
| ENSG00000012963 | UBR7 | 0.6308559 | 5 |
| ENSG00000179222 | MAGED1 | 0.5233699 | 5 |
kable(table_data %>% filter(Module == top_modules[2] & `SFARI score` != 'None') %>% dplyr::select(-Module),
caption=paste0('SFARI Genes for Module ', top_modules[2]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|---|---|---|
| ENSG00000181804 | SLC9A9 | 0.9338530 | 4 |
| ENSG00000014138 | POLA2 | 0.7761639 | 4 |
| ENSG00000128594 | LRRC4 | 0.4352575 | 4 |
| ENSG00000151148 | UBE3B | 0.5519116 | 5 |
| ENSG00000169359 | SLC33A1 | 0.3298546 | 5 |
kable(table_data %>% filter(Module == top_modules[3] & `SFARI score` != 'None') %>% dplyr::select(-Module),
caption=paste0('SFARI Genes for Module ', top_modules[3]))| Ensembl ID | Gene Symbol | Gene Significance | SFARI score |
|---|
Modules with the strongest module-diagnosis correlation should have the highest percentage of SFARI Genes, but this doesn’t seem to be the case
plot_data = dataset %>% mutate('hasSFARIscore' = gene.score!='None') %>%
group_by(Module, MTcor, hasSFARIscore) %>% summarise(p=n()) %>%
left_join(dataset %>% group_by(Module) %>% summarise(n=n()), by='Module') %>%
mutate(p=round(p/n*100,2))
for(i in 1:nrow(plot_data)){
this_row = plot_data[i,]
if(this_row$hasSFARIscore==FALSE & this_row$p==100){
new_row = this_row
new_row$hasSFARIscore = TRUE
new_row$p = 0
plot_data = plot_data %>% rbind(new_row)
}
}
plot_data = plot_data %>% filter(hasSFARIscore==TRUE)
ggplotly(plot_data %>% ggplot(aes(MTcor, p, size=n)) + geom_smooth(color='gray', se=FALSE) +
geom_point(color=plot_data$Module, alpha=0.5, aes(id=Module)) + geom_hline(yintercept=mean(plot_data$p), color='gray') +
xlab('Module-Diagnosis correlation') + ylab('% of SFARI genes') +
theme_minimal() + theme(legend.position = 'none'))rm(i, this_row, new_row, plot_data)Module Eigengenes
Since these modules have the strongest relation to autism, this pattern should be reflected in their model eigengenes, having two different behaviours for the samples corresponding to autism and the ones corresponding to control.
In all cases, the Eigengenes separate the behaviour between autism and control samples very clearly!
plot_EGs = function(module){
plot_data = data.frame('ID' = rownames(MEs), 'MEs' = MEs[,paste0('ME',module)], 'Diagnosis' = datMeta$Diagnosis)
p = plot_data %>% ggplot(aes(Diagnosis, MEs, fill=Diagnosis)) + geom_boxplot() + theme_minimal() + theme(legend.position='none') +
ggtitle(paste0('Module ', module, ' (MTcor=',round(moduleTraitCor[paste0('ME',module),1],2),')'))
return(p)
}
p1 = plot_EGs(top_modules[1])
p2 = plot_EGs(top_modules[2])
p3 = plot_EGs(top_modules[3])
grid.arrange(p1, p2, p3, nrow=1)
rm(plot_EGs, p1, p2)Identifying important genes
Selecting the modules with the highest correlation to Diagnosis, and, from them, the genes with the highest module membership-(absolute) gene significance
*Ordered by \(\frac{MM+|GS|}{2}\)
There aren’t that many SFARI genes in the top genes of the modules and not a single one belonging to scores 1 and 2
create_table = function(module){
top_genes = dataset %>% left_join(genes_info %>% dplyr::select(ID, external_gene_id), by='ID') %>%
dplyr::select(ID, external_gene_id, paste0('MM.',gsub('#','',module)), GS, gene.score) %>%
filter(dataset$Module==module) %>% dplyr::rename('MM' = paste0('MM.',gsub('#','',module))) %>%
mutate(importance = (MM+abs(GS))/2) %>% arrange(by=-importance) %>% top_n(20)
return(top_genes)
}
top_genes_1 = create_table(top_modules[1])
kable(top_genes_1, caption=paste0('Top 10 genes for module ', top_modules[1], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[1]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000124151 | NCOA3 | 0.9176374 | 0.9999000 | None | 0.9587687 |
| ENSG00000137642 | SORL1 | 0.9055291 | 0.9939057 | None | 0.9497174 |
| ENSG00000079156 | OSBPL6 | 0.8472526 | 0.9504339 | None | 0.8988433 |
| ENSG00000161326 | DUSP14 | 0.9015386 | 0.8873720 | None | 0.8944553 |
| ENSG00000198576 | ARC | 0.8706669 | 0.9058439 | None | 0.8882554 |
| ENSG00000166016 | ABTB2 | 0.8894032 | 0.8731209 | None | 0.8812621 |
| ENSG00000146263 | MMS22L | 0.7617517 | 0.9999000 | None | 0.8808258 |
| ENSG00000081923 | ATP8B1 | 0.8139837 | 0.9460922 | None | 0.8800379 |
| ENSG00000172071 | EIF2AK3 | 0.8679807 | 0.8898247 | None | 0.8789027 |
| ENSG00000109971 | HSPA8 | 0.9033004 | 0.8424429 | None | 0.8728717 |
| ENSG00000106069 | CHN2 | 0.8302260 | 0.9149886 | None | 0.8726073 |
| ENSG00000133216 | EPHB2 | 0.8982840 | 0.8375782 | 4 | 0.8679311 |
| ENSG00000122035 | RASL11A | 0.8087259 | 0.9191441 | None | 0.8639350 |
| ENSG00000188306 | LRRIQ4 | 0.7614026 | 0.9637941 | None | 0.8625984 |
| ENSG00000120738 | EGR1 | 0.8579800 | 0.8565483 | None | 0.8572642 |
| ENSG00000125740 | FOSB | 0.8131559 | 0.9002224 | None | 0.8566892 |
| ENSG00000050438 | SLC4A8 | 0.8304395 | 0.8810662 | None | 0.8557529 |
| ENSG00000135537 | LACE1 | 0.8932288 | 0.8105469 | None | 0.8518878 |
| ENSG00000108231 | LGI1 | 0.8507591 | 0.8509201 | None | 0.8508396 |
| ENSG00000138792 | ENPEP | 0.8273163 | 0.8723425 | None | 0.8498294 |
top_genes_2 = create_table(top_modules[2])
kable(top_genes_2, caption=paste0('Top 10 genes for module ', top_modules[2], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[2]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000164168 | TMEM184C | 0.8879874 | 0.9237630 | None | 0.9058752 |
| ENSG00000116857 | TMEM9 | 0.9280139 | 0.8731886 | None | 0.9006012 |
| ENSG00000171135 | JAGN1 | 0.8007814 | 0.9999000 | None | 0.9003407 |
| ENSG00000141524 | TMC6 | 0.7743347 | 0.9582086 | None | 0.8662716 |
| ENSG00000137872 | SEMA6D | 0.8004119 | 0.9293088 | None | 0.8648604 |
| ENSG00000214534 | ZNF705E | 0.7526753 | 0.9394173 | None | 0.8460463 |
| ENSG00000167797 | CDK2AP2 | 0.8649232 | 0.8228135 | None | 0.8438684 |
| ENSG00000204604 | ZNF468 | 0.8488296 | 0.8374370 | None | 0.8431333 |
| ENSG00000143653 | SCCPDH | 0.8068247 | 0.8750338 | None | 0.8409292 |
| ENSG00000181804 | SLC9A9 | 0.7244863 | 0.9338530 | 4 | 0.8291697 |
| ENSG00000163155 | LYSMD1 | 0.8369816 | 0.7986530 | None | 0.8178173 |
| ENSG00000014138 | POLA2 | 0.8463754 | 0.7761639 | 4 | 0.8112697 |
| ENSG00000168917 | SLC35G2 | 0.7991949 | 0.7969682 | None | 0.7980815 |
| ENSG00000137809 | ITGA11 | 0.7219140 | 0.8648469 | None | 0.7933804 |
| ENSG00000249709 | ZNF564 | 0.7461865 | 0.8162660 | None | 0.7812262 |
| ENSG00000111481 | COPZ1 | 0.7158520 | 0.8392657 | None | 0.7775589 |
| ENSG00000106397 | PLOD3 | 0.7313900 | 0.8214711 | None | 0.7764306 |
| ENSG00000184986 | TMEM121 | 0.8580479 | 0.6924940 | None | 0.7752709 |
| ENSG00000140279 | DUOX2 | 0.7465182 | 0.7988820 | None | 0.7727001 |
| ENSG00000175426 | PCSK1 | 0.6929284 | 0.8359477 | None | 0.7644381 |
top_genes_2 = create_table(top_modules[3])
kable(top_genes_2, caption=paste0('Top 10 genes for module ', top_modules[3], ' (MTcor = ',
round(moduleTraitCor[paste0('ME',top_modules[3]),1],2),')'))| ID | external_gene_id | MM | GS | gene.score | importance |
|---|---|---|---|---|---|
| ENSG00000160345 | C9orf116 | 0.9195470 | -0.9108297 | None | 0.9151883 |
| ENSG00000178386 | ZNF223 | 0.8202660 | -0.9953870 | None | 0.9078265 |
| ENSG00000186439 | TRDN | 0.8967728 | -0.8866637 | None | 0.8917183 |
| ENSG00000188312 | CENPP | 0.7657596 | -0.9999000 | None | 0.8828298 |
| ENSG00000137944 | CCBL2 | 0.9051492 | -0.8586605 | None | 0.8819049 |
| ENSG00000114378 | HYAL1 | 0.7642047 | -0.9661230 | None | 0.8651638 |
| ENSG00000146707 | POMZP3 | 0.8031535 | -0.9100025 | None | 0.8565780 |
| ENSG00000163606 | CD200R1 | 0.8933894 | -0.7933818 | None | 0.8433856 |
| ENSG00000196812 | ZSCAN16 | 0.7437446 | -0.9368648 | None | 0.8403047 |
| ENSG00000101898 | RP3-324O17.4 | 0.8346706 | -0.8343198 | None | 0.8344952 |
| ENSG00000105173 | CCNE1 | 0.7561475 | -0.9077603 | None | 0.8319539 |
| ENSG00000101639 | CEP192 | 0.7332188 | -0.9181599 | None | 0.8256893 |
| ENSG00000101213 | PTK6 | 0.7500411 | -0.8513621 | None | 0.8007016 |
| ENSG00000137463 | MGARP | 0.8101799 | -0.7691698 | None | 0.7896748 |
| ENSG00000166347 | CYB5A | 0.7862803 | -0.7866927 | None | 0.7864865 |
| ENSG00000181929 | PRKAG1 | 0.7813053 | -0.7820870 | None | 0.7816962 |
| ENSG00000159618 | GPR114 | 0.7008648 | -0.8202101 | None | 0.7605374 |
| ENSG00000183281 | PLGLB1 | 0.7033820 | -0.7168923 | None | 0.7101371 |
| ENSG00000239887 | C1orf226 | 0.6861897 | -0.7199592 | None | 0.7030744 |
| ENSG00000144741 | SLC25A26 | 0.7355508 | -0.6582607 | None | 0.6969058 |
rm(create_table)pca = datExpr %>% prcomp
plot_data = data.frame('ID'=rownames(datExpr), 'PC1' = pca$x[,1], 'PC2' = pca$x[,2]) %>%
left_join(dataset, by='ID') %>% dplyr::select(ID, PC1, PC2, Module, gene.score) %>%
mutate(color = ifelse(Module %in% top_modules, as.character(Module), 'gray')) %>%
mutate(alpha = ifelse(color %in% top_modules &
ID %in% c(as.character(top_genes_1$ID),
as.character(top_genes_2$ID)), 1, 0.05))
plot_data %>% ggplot(aes(PC1, PC2)) + geom_point(alpha=plot_data$alpha, color=plot_data$color) +
theme_minimal() + ggtitle('Important genes identified through WGCNA')
Enrichment Analysis
Using the package anRichment
It was designed by Peter Langfelder explicitly to perform enrichmen analysis on WGCNA’s modules in brain-related experiments (mainly Huntington’s Disease)
It has packages with brain annotations:
BrainDiseaseCollection: A Brain Disease Gene Set Collection for anRichment
MillerAIBSCollection: (included in anRichment) Contains gene sets collected by Jeremy A. Miller at AIBS of various cell type and brain region marker sets, gene sets collected from expression studies of developing brain, as well as a collection of transcription factor (TF) targets from the original ChEA study
The tutorial says it’s an experimental package
It’s not on CRAN nor in Bioconductor
# Prepare dataset
# Create dataset with top modules membership and removing the genes without an assigned module
EA_dataset = data.frame('ensembl_gene_id' = genes_info$ID,
module = ifelse(genes_info$Module %in% top_modules, genes_info$Module, 'other')) %>%
filter(genes_info$Module!='gray')
# Assign Entrez Gene Id to each gene
getinfo = c('ensembl_gene_id','entrezgene')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl', host='feb2014.archive.ensembl.org')
biomart_output = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=EA_dataset$ensembl_gene_id, mart=mart)## Cache foundEA_dataset = EA_dataset %>% left_join(biomart_output, by='ensembl_gene_id')
for(tm in top_modules){
cat(paste0('\n',sum(EA_dataset$module==tm & is.na(EA_dataset$entrezgene)), ' genes from top module ',
tm, ' don\'t have an Entrez Gene ID'))
}##
## 5 genes from top module #E08B00 don't have an Entrez Gene ID
## 3 genes from top module #7DAE00 don't have an Entrez Gene ID
## 0 genes from top module #FE6E8A don't have an Entrez Gene IDrm(getinfo, mart, biomart_output, tm)# Manual: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/GeneAnnotation/Tutorials/anRichment-Tutorial1.pdf
collectGarbage()
# EA_dataset = rbind(EA_dataset[EA_dataset$module!='other',], EA_dataset[EA_dataset$module=='other',][sample(sum(EA_dataset$module=='other'), 1000),])
# Prepare datasets
GO_col = buildGOcollection(organism = 'human', verbose = 0)## Loading required package: org.Hs.eg.db## ## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columnsinternal_col = internalCollection(organism = 'human')
MillerAIBS_col = MillerAIBSCollection(organism = 'human')
BrainDisease_col = BrainDiseaseCollection(organism = 'human')
combined_col = mergeCollections(GO_col, internal_col, MillerAIBS_col, BrainDisease_col)
# Print collections used
cat('Using collections: ')## Using collections:knownGroups(combined_col, sortBy = 'size')## [1] "GO"
## [2] "GO.BP"
## [3] "GO.MF"
## [4] "GO.CC"
## [5] "JA Miller at AIBS"
## [6] "Chip-X enrichment analysis (ChEA)"
## [7] "Brain"
## [8] "JAM"
## [9] "Prenatal brain"
## [10] "Brain region markers"
## [11] "Cortex"
## [12] "Brain region marker enriched gene sets"
## [13] "WGCNA"
## [14] "BrainRegionMarkers"
## [15] "BrainRegionMarkers.HBA"
## [16] "BrainRegionMarkers.HBA.localMarker(top200)"
## [17] "Postnatal brain"
## [18] "ImmunePathways"
## [19] "Markers of cortex layers"
## [20] "BrainLists"
## [21] "Cell type markers"
## [22] "Germinal brain"
## [23] "BrainRegionMarkers.HBA.globalMarker(top200)"
## [24] "Accelerated evolution"
## [25] "Postmitotic brain"
## [26] "BrainLists.Blalock_AD"
## [27] "BrainLists.DiseaseGenes"
## [28] "BloodAtlases"
## [29] "Verge Disease Genes"
## [30] "BloodAtlases.Whitney"
## [31] "BrainLists.JAXdiseaseGene"
## [32] "BrainLists.MO"
## [33] "Age-associated genes"
## [34] "BrainLists.Lu_Aging"
## [35] "Cell type marker enriched gene sets"
## [36] "BrainLists.CA1vsCA3"
## [37] "BrainLists.MitochondrialType"
## [38] "BrainLists.MO.2+_26Mar08"
## [39] "BrainLists.MO.Sugino"
## [40] "BloodAtlases.Gnatenko2"
## [41] "BloodAtlases.Kabanova"
## [42] "BrainLists.Voineagu"
## [43] "StemCellLists"
## [44] "StemCellLists.Lee"# Perform Enrichment Analysis
enrichment = enrichmentAnalysis(classLabels = EA_dataset$module, identifiers = EA_dataset$entrezgene,
refCollection = combined_col, useBackground = 'given',
threshold = 1e-4, thresholdType = 'Bonferroni',
getOverlapEntrez = FALSE, getOverlapSymbols = TRUE)## enrichmentAnalysis: preparing data..
## ..working on label set 1 ..Results
None of the top modules seem to be enriched in anything! (See Bonferroni and FDR columns)
None of the modules seem to be enriched in anything!!! (lowest p-value in the whole enrichment analysis is 2.6e-5, which becomes 1 after the Bonferroni correction)
kable(enrichment$enrichmentTable %>% filter(class==top_modules[1]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR,
effectiveClassSize, effectiveSetSize, nCommonGenes),
caption = paste0('Enriched terms for module ', top_modules[1], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[1]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|
| JAMiller.AIBS.000526 | Genes bound by TFAP2A in human MCF7 from PMID 17053090 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1 | 1 | 221 | 1388 | 38 |
| GO:0080135 | regulation of cellular response to stress | GO|GO.BP | 1 | 1 | 221 | 656 | 22 |
| GO:2000273 | positive regulation of signaling receptor activity | GO|GO.BP | 1 | 1 | 221 | 35 | 5 |
| JAM:002866 | GlutamatergicNeuronsInMouseCortex_Sugino | JAM|BrainLists|BrainLists.MO|BrainLists.MO.Sugino|Cell type marker enriched gene sets|Brain region marker enriched gene sets | 1 | 1 | 221 | 367 | 15 |
| GO:1900450 | negative regulation of glutamate receptor signaling pathway | GO|GO.BP | 1 | 1 | 221 | 2 | 2 |
| JAM:002967 | Occipital Lobe_IN_Cerebral Cortex | JAM|BrainRegionMarkers|BrainRegionMarkers.HBA|BrainRegionMarkers.HBA.localMarker(top200)|Brain region markers|Brain region marker enriched gene sets | 1 | 1 | 221 | 148 | 9 |
| JAMiller.AIBS.000505 | Genes bound by SUZ12 in MOUSE MESC from PMID 18974828 | JA Miller at AIBS|Chip-X enrichment analysis (ChEA) | 1 | 1 | 221 | 1374 | 35 |
| JAMiller.AIBS.000151 | Lowest in CP of E14.5 mouse | JA Miller at AIBS|Brain|Prenatal brain|Markers of cortex layers|Cortex | 1 | 1 | 221 | 559 | 19 |
| GO:0045833 | negative regulation of lipid metabolic process | GO|GO.BP | 1 | 1 | 221 | 68 | 6 |
| GO:0030515 | snoRNA binding | GO|GO.MF | 1 | 1 | 221 | 26 | 4 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[2]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR,
effectiveClassSize, effectiveSetSize, nCommonGenes),
caption = paste0('Enriched terms for module ', top_modules[2], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[2]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|
| GO:0006493 | protein O-linked glycosylation | GO|GO.BP | 1 | 1 | 144 | 87 | 6 |
| GO:0017176 | phosphatidylinositol N-acetylglucosaminyltransferase activity | GO|GO.MF | 1 | 1 | 144 | 6 | 2 |
| GO:1902231 | positive regulation of intrinsic apoptotic signaling pathway in response to DNA damage | GO|GO.BP | 1 | 1 | 144 | 7 | 2 |
| JAMiller.AIBS.000002 | SZo markers at 21 post-conception weeks | JA Miller at AIBS|Brain|Prenatal brain|Cortex|Markers of cortex layers|Germinal brain | 1 | 1 | 144 | 254 | 8 |
| GO:0000506 | glycosylphosphatidylinositol-N-acetylglucosaminyltransferase (GPI-GnT) complex | GO|GO.CC | 1 | 1 | 144 | 8 | 2 |
| GO:0036066 | protein O-linked fucosylation | GO|GO.BP | 1 | 1 | 144 | 8 | 2 |
| GO:0006664 | glycolipid metabolic process | GO|GO.BP | 1 | 1 | 144 | 103 | 5 |
| GO:0042287 | MHC protein binding | GO|GO.MF | 1 | 1 | 144 | 30 | 3 |
| GO:1903509 | liposaccharide metabolic process | GO|GO.BP | 1 | 1 | 144 | 104 | 5 |
| GO:0048478 | replication fork protection | GO|GO.BP | 1 | 1 | 144 | 9 | 2 |
kable(enrichment$enrichmentTable %>% filter(class==top_modules[3]) %>%
dplyr::select(dataSetID, shortDataSetName, inGroups, Bonferroni, FDR,
effectiveClassSize, effectiveSetSize, nCommonGenes),
caption = paste0('Enriched terms for module ', top_modules[3], ' (MTcor = ',
round(genes_info$MTcor[genes_info$Module==top_modules[3]][1],4), ')'))| dataSetID | shortDataSetName | inGroups | Bonferroni | FDR | effectiveClassSize | effectiveSetSize | nCommonGenes |
|---|---|---|---|---|---|---|---|
| GO:0043062 | extracellular structure organization | GO|GO.BP | 1 | 1 | 30 | 352 | 5 |
| GO:0000095 | S-adenosyl-L-methionine transmembrane transporter activity | GO|GO.MF | 1 | 1 | 30 | 1 | 1 |
| GO:0002225 | positive regulation of antimicrobial peptide production | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
| GO:0002760 | positive regulation of antimicrobial humoral response | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
| GO:0002784 | regulation of antimicrobial peptide production | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
| GO:0002786 | regulation of antibacterial peptide production | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
| GO:0002803 | positive regulation of antibacterial peptide production | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
| GO:0015805 | S-adenosyl-L-methionine transport | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
| GO:0047315 | kynurenine-glyoxylate transaminase activity | GO|GO.MF | 1 | 1 | 30 | 1 | 1 |
| GO:1901846 | positive regulation of cell communication by electrical coupling involved in cardiac conduction | GO|GO.BP | 1 | 1 | 30 | 1 | 1 |
Save Enrichment Analysis results
save(enrichment, file='./../Data/enrichmentAnalysis.RData')Session info
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-redhat-linux-gnu (64-bit)
## Running under: Scientific Linux 7.6 (Nitrogen)
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/R/lib/libRblas.so
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] org.Hs.eg.db_3.10.0
## [2] BrainDiseaseCollection_1.00
## [3] anRichment_1.01-2
## [4] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [5] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [6] GenomicFeatures_1.38.2
## [7] GenomicRanges_1.38.0
## [8] GenomeInfoDb_1.22.0
## [9] anRichmentMethods_0.90-1
## [10] WGCNA_1.68
## [11] fastcluster_1.1.25
## [12] dynamicTreeCut_1.63-1
## [13] GO.db_3.10.0
## [14] AnnotationDbi_1.48.0
## [15] IRanges_2.20.2
## [16] S4Vectors_0.24.3
## [17] Biobase_2.46.0
## [18] BiocGenerics_0.32.0
## [19] biomaRt_2.42.0
## [20] knitr_1.24
## [21] doParallel_1.0.15
## [22] iterators_1.0.12
## [23] foreach_1.4.7
## [24] polycor_0.7-10
## [25] expss_0.10.1
## [26] GGally_1.4.0
## [27] gridExtra_2.3
## [28] viridis_0.5.1
## [29] viridisLite_0.3.0
## [30] RColorBrewer_1.1-2
## [31] dendextend_1.13.3
## [32] plotly_4.9.2
## [33] glue_1.3.1
## [34] reshape2_1.4.3
## [35] forcats_0.4.0
## [36] stringr_1.4.0
## [37] dplyr_0.8.3
## [38] purrr_0.3.3
## [39] readr_1.3.1
## [40] tidyr_1.0.2
## [41] tibble_2.1.3
## [42] ggplot2_3.2.1
## [43] tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.5
## [3] Hmisc_4.2-0 BiocFileCache_1.10.2
## [5] plyr_1.8.5 lazyeval_0.2.2
## [7] splines_3.6.0 crosstalk_1.0.0
## [9] BiocParallel_1.20.1 robust_0.4-18.2
## [11] digest_0.6.24 htmltools_0.4.0
## [13] fansi_0.4.1 magrittr_1.5
## [15] checkmate_1.9.4 memoise_1.1.0
## [17] fit.models_0.5-14 cluster_2.0.8
## [19] annotate_1.64.0 Biostrings_2.54.0
## [21] modelr_0.1.5 matrixStats_0.55.0
## [23] askpass_1.1 prettyunits_1.0.2
## [25] colorspace_1.4-1 blob_1.2.1
## [27] rvest_0.3.5 rappdirs_0.3.1
## [29] rrcov_1.4-7 haven_2.2.0
## [31] xfun_0.8 crayon_1.3.4
## [33] RCurl_1.95-4.12 jsonlite_1.6
## [35] genefilter_1.68.0 impute_1.60.0
## [37] survival_2.44-1.1 gtable_0.3.0
## [39] zlibbioc_1.32.0 XVector_0.26.0
## [41] DelayedArray_0.12.2 DEoptimR_1.0-8
## [43] scales_1.1.0 mvtnorm_1.0-11
## [45] DBI_1.1.0 Rcpp_1.0.3
## [47] xtable_1.8-4 progress_1.2.2
## [49] htmlTable_1.13.1 foreign_0.8-71
## [51] bit_1.1-15.2 preprocessCore_1.48.0
## [53] Formula_1.2-3 htmlwidgets_1.5.1
## [55] httr_1.4.1 ellipsis_0.3.0
## [57] acepack_1.4.1 farver_2.0.3
## [59] pkgconfig_2.0.3 reshape_0.8.8
## [61] XML_3.99-0.3 nnet_7.3-12
## [63] dbplyr_1.4.2 locfit_1.5-9.1
## [65] later_1.0.0 labeling_0.3
## [67] tidyselect_0.2.5 rlang_0.4.4
## [69] munsell_0.5.0 cellranger_1.1.0
## [71] tools_3.6.0 cli_2.0.1
## [73] generics_0.0.2 RSQLite_2.2.0
## [75] broom_0.5.4 fastmap_1.0.1
## [77] evaluate_0.14 yaml_2.2.0
## [79] bit64_0.9-7 fs_1.3.1
## [81] robustbase_0.93-5 nlme_3.1-139
## [83] mime_0.9 xml2_1.2.2
## [85] compiler_3.6.0 rstudioapi_0.10
## [87] curl_4.3 reprex_0.3.0
## [89] geneplotter_1.64.0 pcaPP_1.9-73
## [91] stringi_1.4.6 highr_0.8
## [93] lattice_0.20-38 Matrix_1.2-17
## [95] vctrs_0.2.2 pillar_1.4.3
## [97] lifecycle_0.1.0 data.table_1.12.8
## [99] bitops_1.0-6 httpuv_1.5.2
## [101] rtracklayer_1.46.0 R6_2.4.1
## [103] latticeExtra_0.6-28 promises_1.1.0
## [105] codetools_0.2-16 MASS_7.3-51.4
## [107] assertthat_0.2.1 SummarizedExperiment_1.16.1
## [109] DESeq2_1.26.0 openssl_1.4.1
## [111] withr_2.1.2 GenomicAlignments_1.22.1
## [113] Rsamtools_2.2.2 GenomeInfoDbData_1.2.2
## [115] hms_0.5.3 grid_3.6.0
## [117] rpart_4.1-15 rmarkdown_1.14
## [119] Cairo_1.5-10 shiny_1.4.0
## [121] lubridate_1.7.4 base64enc_0.1-3